While visiting fully self-managed company Rebel Group, we learned about 4 powerful lessons to successfully run a self-managed company. Here they are.

A few months ago we received a message from one of the co-founders of a Dutch consultancy firm explaining us their unique way of working. Their name? Rebel, what a coincidence! With such a beautiful name it seemed just a matter of time for us to visit them and learn more about their unique approach of organizing the firm. So, a few months later we find ourselves travelling to the Dutch city of Rotterdam where Rebel has its headquarters. Together with co-founder Jeroen in ‘t Veld and the recently joined Rachida Abdellaoui we explored their unique way of working. During the visit we discussed at length how the leadership of the firm passes on the culture and vision to the company’s new generation of rebels. But what is it that we can learn from this rebellious consultancy firm?

Abstract Thinking of agent-based models in terms of artifacts, useful to explore economic complexity, means introducing three concepts: on a technical side, an agent-based methodology; in a social sciences perspective, the idea of building artifacts also within the social domain; in a more general view, the idea of complexity. Bearing in mind the theoretical roots of cybernetics and, more recently, of complexity science, we need also technical roots, with the capability of building models that are acceptable to a wide audience, of comparing classic and new models, and of proposing hybrid structures as well. Finally, we need to pay attention to the agents’ abilities, mainly in mimicking the human capability for learning and adapting.

Over the decades, combinations of various programming techniques have enabled slow spotty progress in AI — punctuated by occasional breakthroughs such as certain expert, decision and planning systems, and mastering Chess and Jeopardy! These approaches, and in particular those focused on symbolic representations, are generally referred to as GOFAI (Good Old-Fashioned AI). Importantly, a key characteristic that they share is that applications are hand-crafted and custom engineered: Programmers figure out how to solve a particular problem, then turning their insights into code. This essentially represents the ‘First Wave’.

In this paper we explore several fundamental relations between formal systems, algorithms, and dynamical systems, focussing on the roles of undecidability, universality, diagonalization, and self-reference in each of these computational frameworks. Some of these interconnections are well-known, while some are clarified in this study as a result of a fine-grained comparison between recursive formal systems, Turing machines, and Cellular Automata (CAs). In particular, we elaborate on the diagonalization argument applied to distributed computation carried out by CAs, illustrating the key elements of G\"odel's proof for CAs. The comparative analysis emphasizes three factors which underlie the capacity to generate undecidable dynamics within the examined computational frameworks: (i) the program-data duality; (ii) the potential to access an infinite computational medium; and (iii) the ability to implement negation. The considered adaptations of G\"odel's proof distinguish between computational universality and undecidability, and show how the diagonalization argument exploits, on several levels, the self-referential basis of undecidability.

Abstract Economic theory suggests that, in most circumstances, market forces will ensure that stan-dard form contracts contain terms that are not only socially efficient but also beneficial to non-drafting parties as a class compared to other possible combinations of price and terms. This analy-sis in turn suggests that courts should enforce all form terms or, at a minimum, all form terms that non-drafting parties read and understand. Relying on social science research on decisionmaking, this Article argues that non-drafting parties (usually buyers) are boundedly rational decisionmak-ers who will normally price only a limited number of product attributes as part of their purchase decision. When contract terms are not among these attributes, drafting parties will have a market incentive to include terms in their standard forms that favor themselves, whether or not such terms are efficient. Thus, there is no a priori reason to assume form contract terms will be efficient. The Article then argues that the proper policy response to this conclusion is greater use of mandatory contract terms and judicial modification of the unconscionability doctrine to better respond to the primary cause of contractual inefficiency.

Abstract This report reflects the combined efforts of one subgroup of an assemblage of distinguished scholars in law, business, economics, psychology, and neuroscience who gathered for a week in Berlin in 2004 as part of the 94th Dahlem Workshop on Heuristics and the Law. This subgroup was moderated by Robert Frank (Cornell University), and included Peter Ayton (City University - London), Bruno Frey (University of Zurich), Gerd Gigerenzer (Max Planck Institute for Human Development), Paul Glimcher (New York University), Russell Korobkin (University of California, Los Angeles), Donald Langevoort (Georgetown University), and Stefan Magen (Max Planck Institute for Research on Collective Goods). Douglas Kysar (Cornell University) served as rapporteur. Charged with addressing the theme, Are Heuristics a Problem or a Solution?, the subgroup discussed and debated a range of methodological, descriptive, and prescriptive issues concerning the implications of cognitive psychology for law, many of which are summarized in this consensus report. Included are (1) a general introduction to the subject of heuristics in decision theory, with particular attention to the distinction between optimality-based and heuristic-based decision making models within psychology; (2) an attempt to synthesize these two psychological research paradigms into a single conceptual framework that helps to identify important areas in which further research and understanding are needed; (3) an overview of scholarship to date on heuristics and the law, including the observation that this scholarship has ignored certain significant lessons of the heuristics research tradition in psychology; and (4) a compilation of suggestions for future interdisciplinary research concerning both the use of heuristics by legal subjects whose behavior the law is attempting to influence, and the use of heuristics by policymakers as a model for the substantive design of legal rules.

Abstract In recent years, policy makers worldwide have begun to acknowledge the potential value of insights from psychology and behavioral economics into how people make decisions. These insights can inform the design of nonregulatory and nonmonetary policy interventions—as well as more traditional fiscal and coercive measures. To date, much of the discussion of behaviorally informed approaches has emphasized “nudges,” that is, interventions designed to steer people in a particular direction while preserving their freedom of choice. Yet behavioral science also provides support for a distinct kind of nonfiscal and noncoercive intervention, namely, “boosts.” The objective of boosts is to foster people’s competence to make their own choices—that is, to exercise their own agency. Building on this distinction, we further elaborate on how boosts are conceptually distinct from nudges: The two kinds of interventions differ with respect to (a) their immediate intervention targets, (b) their roots in different research programs, (c) the causal pathways through which they affect behavior, (d) their assumptions about human cognitive architecture, (e) the reversibility of their effects, (f) their programmatic ambitions, and (g) their normative implications. We discuss each of these dimensions, provide an initial taxonomy of boosts, and address some possible misconceptions.

“FREAKONOMICS” was the book that made the public believe the dismal science has something interesting to say about how people act in the real world. But “Nudge” was the one that got policy wonks excited.

In my last article we covered the business case for behavioural economics (BE), including how to engage and convince your stakeholders. Now let’s consider how best to resource your BE function. Where should your BE function sit? Make no mistake, BE is a workplace-wide opportunity. In other words, behavioural techniques can be applied to how your receptionist answers the phone, how your invoices are designed, a proposal or tender document and presentation, supplier negotiations, pricing strategy, product design, marketing campaigns…even how the staff cafeteria is set up. But let’s not get overwhelmed. Frequent readers will know that too many options can result in decision paralysis! So instead let’s start with some clear decisions about where the BE function should sit. Make no mistake, behavioural economics is a workplace-wide opportunity and can be applied to every aspect of your organisation.

The American economist Richard H. Thaler is a pioneer in behavioural economics, a research field in which insights from psychological research are applied to economic decision-making. A behavioural perspective incorporates more realistic analysis of how people think and behave when making economic decisions, providing new opportunities for designing measures and institutions that increase societal benefit. Economics involves understanding human behaviour in economic decision-making situations and in markets. People are complicated beings, and we must make simplifying assumptions if we are to build useful models. Traditional economic theory assumes that people have good access to information and can process it perfectly. It also assumes that we can always execute our plans and that we only care about personal gain. This simplified model of human behaviour has helped economists to formulate theories that have provided solutions to important and complicated economic problems. However, the discrepancies between theory and reality are sometimes both systematic and significant. Richard Thaler has contributed to expanding and refining economic analysis by considering three psychological traits that systematically influence economic decisions – limited rationality, perceptions about fairness, and lack of self-control. Limited rationality

Over the past 10,000 years human societies evolved from “simple” – small egalitarian groups, integrated by face-to-face interactions – to “complex” – huge anonymous societies of millions, characterized by great differentials in wealth and power, extensive division of labor, elaborate governance structures, and sophisticated information systems. What were the evolutionary processes that brought about such an enormous increase in social scale and complexity? We also need to understand why social forces that hold huge human societies together sometimes fail to do so. Complex societies collapsed on numerous occasions in the past, and may be at risk today. There are clear signs that even industrialized, wealthy, and democratic Western societies, that seemed to be immune to collapse until recently, are becoming less stable. Research on social complexity will bring understanding that is of direct value to our societies and human well-being.

This is the age of big data. We are constantly in quest of more numbers and more complex algorithms to crunch them. We seem to believe that this will solve most of the world’s problems - in economy, society and even our personal lives. As a corollary, rules of thumb and gut instincts are getting a short shrift. We think they often violate the principles of logic and lead us into making bad decisions. We might have had to depend on heuristics and our gut feelings in agricultural and manufacturing era. But this is digital age. We can optimise everything. Can we? Gerd Gigerenzer, a sixty nine year German psychologist who has been studying how humans make decisions for most of his career, doesn't think so. In the real world, rules of thumb not only work well, they also perform better than complex models, he says. We shouldn’t turn our noses up on heuristics, we should embrace them.

The banking industry has become increasingly concerned over the challenge that emerging fintech startups pose to banks’ traditional ways of doing business and the threat that they present to revenue streams. In response, many banks have created internal innovation labs to counter these risks. “Design thinking” has become an important tool in the effort and is being used to explore how banks can boost their growth by applying the approach in a rapidly changing environment and an era of de-banking.

Falling oil revenues and rapid urbanization are putting a strain on the budgets of oil producing nations which often subsidize domestic fuel consumption. A direct way to decrease the impact of subsidies is to reduce fuel consumption by reducing congestion and car trips. While fuel consumption models have started to incorporate data sources from ubiquitous sensing devices, the opportunity is to develop comprehensive models at urban scale leveraging sources such as Global Positioning System (GPS) data and Call Detail Records. We combine these big data sets in a novel method to model fuel consumption within a city and estimate how it may change due to different scenarios. To do so we calibrate a fuel consumption model for use on any car fleet fuel economy distribution and apply it in Riyadh, Saudi Arabia. The model proposed, based on speed profiles, is then used to test the effects on fuel consumption of reducing flow, both randomly and by targeting the most fuel inefficient trips in the city. The estimates considerably improve baseline methods based on average speeds, showing the benefits of the information added by the GPS data fusion. The presented method can be adapted to also measure emissions. The results constitute a clear application of data analysis tools to help decision makers compare policies aimed at achieving economic and environmental goals.

There isn't one specific pattern that emerges from self-organization. The processes are so deep and fundamental to human interactions that you cannot enforce any specific hierarchical or non-hierarchical pattern with rules. Trust between people is an outcome of allowing people to freely self-organize. Complex networks of trust emerge and change as people continuously negotiate their relationships.

Psychology and economics (the mixture of which is known as behavioral economics) are two fundamental disciplines underlying marketing. Various marketing studies document the nonrational behavior of consumers, even though behavioral biases might not always be consistently termed or formally described. In this review, we identify empirical research that studies behavioral biases in marketing. We summarize the key findings according to three classes of deviations (i.e., non-standard preferences, non-standard beliefs, and non-standard decisionmaking) and the marketing mix instruments (i.e., product, price, place, and promotion). We thereby introduce marketing researchers to the theoretical foundation of and terminology used in behavioral economics. For scholars from behavioral economics, we provide ready access to the rich empirical, applied marketing literature. We conclude with important managerial implications resulting from the behavioral biases of consumers, and we present avenues for future research.

Behavioral economics is changing our understanding of how economic policy operates – including tax policy. In this paper, William J. Congdon, Jeffrey R. Kling and Sendhil Mullainathan consider some implications of behavioral economics for tax policy, such as how it changes our understanding of the welfare consequences of taxation, the relative desirability of using the tax system as a platform for policy implementation and the role of taxes as an element of policy design.

INTRODUCTION Neoclassical law and economics analysis, preoccupied with the normative goal of maximizing efficiency, assiduously avoids paternalism as a justification for regulatory policy. Built on the edifice of rational choice theory, law and economics scholars usually assume that economic actors are able to maximize the satisfaction of their preferences given the constraints they face.1. Evidence gathered by psychologists and behavioral economists about human decision making over the last three decades has raised a serious challenge to the rational actor assumption of neoclassical economics. It turns out that most people routinely fail to make optimal decisions-understood as those that maximize the actor's subjective expected utility ("SEU")-in a variety of contexts. The world is too complex for our brains to accurately and reliably calculate expected utility in strategic environments. Instead, humans rely on mental heuristics and habits, which allow us to function in the workaday world without being paralyzed by information overload. The result is that we stumble crudely through life, remaining on our feet most of the time but often enjoying less utility than is theoretically possible. These findings, imported into normative legal theory as behavioral law and economics, expand the potential space for paternalistic state intervention.

Traditional public finance provides a powerful framework for policy analysis, but it relies on a model of human behavior that the new science of behavioral economics increasingly calls into question. In Policy and Choice economists William Congdon, Jeffrey Kling, and Sendhil Mullainathan argue that public finance not only can incorporate many lessons of behavioral economics but also can serve as a solid foundation from which to apply insights from psychology to questions of economic policy. The authors revisit the core questions of public finance, armed with a richer perspective on human behavior. They do not merely apply findings from psychology to specific economic problems; instead, they explore how psychological factors actually reshape core concepts in public finance such as moral hazard, deadweight loss, and incentives. Part one sets the stage for integrating behavioral economics into public finance by interpreting the evidence from psychology and developing a framework for applying it to questions in public finance. In part two, the authors apply that framework to specific topics in public finance, including social insurance, externalities and public goods, income support and redistribution, and taxation.

Downloadable! Despite the great effort that has been dedicated to the attempt to redefine expected utility theory on the grounds of new assumptions, modifying or moderating some axioms, none of the alternative theories propounded so far had a statistical confirmation over the full domain of applicability. Moreover, the discrepancy between prescriptions and behaviors is not limited to expected utility theory. In two other fundamental fields, probability and logic, substantial evidence shows that human activities deviate from the prescriptions of the theoretical models. The paper suggests that the discrepancy cannot be ascribed to an imperfect axiomatic description of human choice, but to some more general features of human reasoning and assumes the ï¿½dual-process account of reasoningï¿½ as a promising explanatory key. This line of thought is based on the distinction between the process of deliberate reasoning and that of intuition; where in a first approximation, ï¿½intuitionï¿½ denotes a mental activity largely automatized and inaccessible from conscious mental activity. The analysis of the interactions between these two processes provides the basis for explaining the persistence of the gap between normative and behavioral patterns. This view will be explored in the following pages: central consideration will be given to the problem of the interactions between rationality and intuition, and the correlated ï¿½modularityï¿½ of the thought.

The golden promise of the internet was the creation of an Eden of transparency and efficiency. We would be able to compare prices for all manner of goods and services at the click of a mouse button. It would complete markets and empower customers. To an extent the promise has been fulfilled. Anyone old enough to remember what it was like searching for an out-of-print book or reserving a flight before the days of the internet will know what technological progress feels like.

How you go about embedding behavioural economics in your business will, of course, be shaped by where you want it to fit within the organisation.

This is the third and final instalment on embedding behavioural economics (BE) in your business. Part one looked at building a business case for behavioural techniques and part two covered where your BE function should sit in your organisation. In part three we will be examining how to go about actually embedding BE – where to start and what to do. How you go about embedding BE in your business will, of course, be shaped by where you want it to fit within the organisation (e.g. centralised or decentralised, in-house or outsourced). But whether you are recruiting a BE specialist or using external expertise to up skill your team, there’s a roadmap you might like to follow.

Cooperation among animals is ubiquitous. In a cooperative interaction, the cooperator confers a benefit to its partner at a personal cost. How does natural selection favour such a costly behaviour? Classical theories argue that cooperative interactions among genetic relatives, reciprocal cooperators, or among individuals within groups in viscous population structures are necessary to maintain cooperation. However, many organisms are mobile, and live in dynamic (fission-fusion) groups that constantly merge and split. In such populations, the above mechanisms may be inadequate to explain cooperation. Here, we develop a minimal model that explicitly accounts for mobility and cohesion among organisms. We find that mobility can support cooperation via emergent dynamic groups, even in the absence of previously known mechanisms. Our results may offer insights into the evolution of cooperation in animals that live in fission fusion groups, such as birds, fish or mammals, or microbes living in turbulent media, such as in oceans or in the bloodstreams of animal hosts.

Cooperation is always explained by Hamilton's rule (Benefit > Relationship*Cost) but that does not hold when mobility is taken into account. Here a model is presented that deals with mobility. Although modeled for animals, this obviously goes for Human society as well.

Complexity science shows us not only what to do, but also how to do it: build shared infrastructure, improve information flow, enable rapid innovation, encourage participation, support diversity and citizen empowerment.

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